docstr: Set up the unit test by loading the dataset and training a model.
docstr_tok: ["Set","up","the","unit","test","by","loading","the","dataset","and","training","a","model","."]
code: def setUpClass(self):
      """
      Set up the unit test by loading the dataset and training a model.
      """
      if not _HAS_XGBOOST:
          return
      if not _HAS_SKLEARN:
          return
  
      scikit_data = load_boston()
      dtrain = xgboost.DMatrix(
          scikit_data.data,
          label=scikit_data.target,
          feature_names=scikit_data.feature_names,
      )
      xgb_model = xgboost.train({}, dtrain, 1)
  
      # Save the data and the model
      self.scikit_data = scikit_data
      self.xgb_model = xgb_model
      self.feature_names = self.scikit_data.feature_names
  
      # train a booster with special characters in feature names
      x = scikit_data["data"]
      # prepare feature names with special chars
      self.feature_names_special_chars = [
          f'\t"{i}"\n' for i in range(x.shape[1])
      ]
      # create training dmatrix
      dm = xgboost.DMatrix(
          x,
          label=scikit_data.target,
          feature_names=self.feature_names_special_chars,
      )
      # train booster
      self.xgb_model_special_chars = xgboost.train({}, dm, 1)
      # create XGBClassifier from a copy of trainer booster
      self.xgb_regressor_special_chars = xgboost.XGBRegressor(
          xgb_model=self.xgb_model_special_chars.copy(), n_estimators=1
      )
      self.xgb_regressor_special_chars.fit(x, scikit_data.target)
  
file_path: coremltools/coremltools/test/xgboost_tests/test_boosted_trees_regression.py
imports: import json
  import tempfile
  import unittest
  from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST
  from coremltools.models.utils import _macos_version
label_desc: Use image node_id 1 for calling the BoostedTreeRegressorXGboostTest obj's underlying member method code with example usage: obj.setUpClass() without return types